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arXiv · 2310.08417

Optimized continuous dynamical decoupling via differential geometry and machine learning

Nicolas André da Costa Morazotti·Adonai Hilário da Silva·Gabriel Audi·Felipe Fernandes Fanchini·Reginaldo de Jesus Napolitano
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Abstract

We introduce a strategy to develop optimally designed fields for continuous dynamical decoupling. Using our methodology, we obtain the optimal continuous field configuration to maximize the fidelity of a general one-qubit quantum gate. To achieve this, considering dephasing-noise perturbations, we employ an auxiliary qubit instead of the boson bath to implement a purification scheme, which results in unitary dynamics. Employing the sub-Riemannian geometry framework for the two-qubit unitary group, we derive and numerically solve the geodesic equations, obtaining the optimal time-dependent control Hamiltonian. Also, due to the extended time required to find solutions to the geodesic equations, we train a neural network on a subset of geodesic solutions, enabling us to promptly generate the time-dependent control Hamiltonian for any desired gate, which is crucial in circuit optimization.

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Nicolas André da Costa Morazotti, Adonai Hilário da Silva, Gabriel Audi, Felipe Fernandes Fanchini, Reginaldo de Jesus Napolitano. 2023-10-12. Optimized continuous dynamical decoupling via differential geometry and machine learning. https://doi.org/10.1103/physreva.110.042601

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